In order to rapidly and accurately achieve the identification of the real or fake blood, as well as recycled usage of blood, the photoacoustic spectroscopy was used in this work to establish a set of blood photoacoustic detection systems and to capture the photoacoustic signal of blood samples. Three kinds of animal blood (horse blood, cow blood, and rabbit blood), two kinds of fake blood (props blood and red ink), the total number of blood samples are 125 groups, were used as the experimental samples. The photoacoustic signals and photoacoustic peak-to-peak spectral of all blood samples at 700~1 064 nm were obtained. Photoacoustic experimental results show that the amplitude, profile, peak-point time, and peak-to-peak values of real and fake blood samples are different. To achieve the classification and identification of the real and fake blood with high precision, we used the wavelet neural network optimized by a genetic algorithm (WNN-GA) to train the 100 groups of samples for five kinds of blood in full wavelengths. Moreover, a kind of Morlet-like wavelet basis function was built. Then, 25 groups of blood samples were tested. Meanwhile, the GA algorithm was used to optimize the weights and thresholds of WNN network and the shift factor and stretch factor of wavelet basis function, and two learn factors can be adjusted. Compared with WNN, the correction rate of classification and identification for real and fake blood based on WNN-GA improved by 24%. Then, the principle components analysis (PCA) algorithm was used to extract the characteristic information of real or fake blood from the photoacoustic peak-to-peak full spectral. After that, the chosen principle components were trained and test by the WNN-GA algorithm. Results show that under 6 principle components, the algorithm of PCA-WNN-GA algorithm improves the correction rate of classification and identification for real and fake blood to 100%. Finally, compared with other the six algorithms, the correction rate of classification and identification for PCA-WNN-GA was superior to others. Therefore, the classification and identification of the real and fake blood can be well achieved via photoacoustic spectroscopy combined with the PCA-WNN-GA algorithm.